Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning
Machine learning in remote sensing has matured alongside a proliferation in availability and resolution of geospatial imagery, but its utility is bottlenecked by the need for labeled data. What's more, many labeled geospatial datasets are specific to certain regions, instruments, or extreme wea...
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Zusammenfassung: | Machine learning in remote sensing has matured alongside a proliferation in
availability and resolution of geospatial imagery, but its utility is
bottlenecked by the need for labeled data. What's more, many labeled geospatial
datasets are specific to certain regions, instruments, or extreme weather
events. We investigate the application of modern domain-adaptation to multiple
proposed geospatial benchmarks, uncovering unique challenges and proposing
solutions to them. |
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DOI: | 10.48550/arxiv.2107.04983 |